Automatically determining a current value for a real estate property, such as a home, that is tailored to input from a human user, such as its owner

ABSTRACT

A facility procuring information about a distinguished property from its owner that is usable to refine an automatic valuation of the distinguished property is described. The facility displays information about the distinguished property used in the automatic valuation of the distinguished property. The facility obtains user input from the owner adjusting at least one aspect of information about the distinguished property used in the automatic valuation of the distinguished property. The facility then displays to the owner a refined valuation of the distinguished property that is based on the adjustment of the obtained user input.

TECHNICAL FIELD

The described technology is directed to the field of electronic commercetechniques, and, more particularly, to the field of electronic commercetechniques relating to real estate.

BACKGROUND

In many roles, it can be useful to be able to accurately determine thevalue of real estate properties (“properties”), such as residential realestate properties (“homes”). As examples, by using accurate values forhomes: taxing bodies can equitably set property tax levels; sellers andtheir agents can optimally set listing prices; and buyers and theiragents can determine appropriate offer amounts.

A variety of conventional approaches exist for valuing homes. Perhapsthe most reliable is, for a home that was very recently sold,attributing its selling price as its value. Unfortunately, following thesale of a home, its current value can quickly diverge from its saleprice. Accordingly, the sale price approach to valuing a home tends tobe accurate for only a short period after the sale occurs. For thatreason, at any given time, only a small percentage of homes can beaccurately valued using the sale price approach.

Another widely-used conventional approach to valuing homes is appraisal,where a professional appraiser determines a value for a home bycomparing some of its attributes to the attributes of similar nearbyhomes that have recently sold (“comps”). The appraiser arrives at anappraised value by subjectively adjusting the sale prices of the compsto reflect differences between the attributes of the comps and theattributes of the home being appraised. The accuracy of the appraisalapproach can be adversely affected by the subjectivity involved. Also,appraisals can be expensive, can take days or weeks to complete, and mayrequire physical access to the home by the appraiser.

While it might be possible to design systems that automatically valuehomes, such automatic valuations would generally be performed based uponthe contents of a public database, and without input from each home'sowner or other information not in the public database. In such systems,failing to consider such information may result in valuations that aresignificantly inaccurate in some instances.

In view of the shortcomings of conventional approaches to valuing homesdiscussed above, a new approach to valuing homes that was responsive toowner input, as well as having a high level of accuracy, and beinginexpensive and convenient, would have significant utility.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing some of the components typicallyincorporated in at least some of the computer systems and other deviceson which the facility executes.

FIG. 2 is a flow diagram showing steps typically performed by thefacility to automatically determine current values for homes in ageographic area.

FIG. 3 is a table diagram showing sample contents of a recent salestable.

FIG. 4A is a flow diagram showing steps typically performed by thefacility in order to construct a tree.

FIG. 4B is a flow diagram showing steps typically performed by thefacility in order to determine whether and how to split a node of atree.

FIG. 5 is a table diagram showing sample contents of a basis tablecontaining the basis information selected for the tree.

FIG. 6 is a tree diagram showing a root node corresponding to the basistable 500.

FIG. 7 is a tree diagram showing a completed version of the sample tree.

FIG. 8 is a flow diagram showing steps typically performed by thefacility in order to score a tree.

FIG. 9 is a table diagram showing sample results for scoring a tree.

FIG. 10 is a display diagram showing detailed information about anindividual home.

FIG. 11 is a display diagram showing a map identifying a number of homesin the same geographic area.

FIG. 12 is a display diagram showing a display typically presented bythe facility containing the attributes of a particular home.

FIG. 13 is a display diagram showing a display typically presented bythe facility to identify possible comparable sales on a map.

FIG. 14 is a flow diagram showing steps typically performed by thefacility in order to tailor a valuation of a subject home based oninformation provided by a user such as the home's owner.

FIG. 15 is a display diagram showing a sample display typicallypresented by the facility to display an initial valuation of the subjecthome and solicit updated home attributes from the user.

FIG. 16 is a display diagram showing a typical display presented by thefacility to permit the user to describe improvements made to the subjecthome.

FIG. 17 is a display diagram showing a sample display typicallypresented by the facility to enable the user to describe other aspectsof the subject home that affect its value.

FIG. 18 is a display diagram showing a sample display presented by thefacility in order to enable the user to identify comps regarded by theowner as similar to the subject home.

FIGS. 19A-19F show a sample display typically presented by the facilityin order to present an overall revised value for the subject home.

FIG. 20 is a table diagram showing sample contents of recent salesinformation used to construct a linear regression valuation model thatis based on the attributes whose values are available for the user toupdate in the first step of the process of generating a tailoredvaluation.

DETAILED DESCRIPTION

Overview

A software facility for automatically determining a current value for ahome or other property that is tailored to input from its owner oranother user (“the facility”) is described. While the followingdiscussion liberally employs the word “home” to refer to the propertybeing valued in other nearby properties, those skilled in the art willappreciate that the facility may be straightforwardly applied toproperties of other types. Similarly, while a wide variety of users mayuse the facility, including the owner, an agent or other personrepresenting the owner, a prospective buyer, an agent or other personrepresenting prospective buyer, or another third party.

In some embodiments, the facility uses a web site to receive informationfrom a user and display to the user a refined valuation for the homethat is based upon the information provided by the user. In someembodiments, the information provided by the user may includeadditional, corrected, and/or updated attributes of the home relative tothe attributes known by the facility, such as attributes retrieved bythe facility from a public or private database of home attributes;information about improvements to the home; information about otherfactors likely to affect the value of the home, such as well-keptgrounds, historical significance, ground water issues, etc.; andinformation identifying, among recent, nearby sales of comparable homes(“comps”), those that the user regards as the most similar to thesubject home. In some embodiments, the facility displays the results ofrefining its valuation in a manner that makes clear how the valuationwas affected by the different information provided by the user.

By enabling an user to refine a valuation of his or her home based uponinformation about the home known to the user, the facility in many casesmakes the valuation more accurate than would otherwise be possible,and/or helps the user to more fully accept the valuation as appropriate.

Home Valuation

In some embodiments, the facility constructs and/or applies housingprice models each constituting a forest of classification trees. In somesuch embodiments, the facility uses a data table that identifies, foreach of a number of homes recently sold in the geographic region towhich the forest corresponds, attributes of the home and its sellingprice. For each of the trees comprising the forest, the facilityrandomly selects a fraction of homes identified in the table, as well asa fraction of the attributes identified in the table. The facility usesthe selected attributes of the selected homes, together with the sellingprices of the selected homes, to construct a classification tree inwhich each non-leaf node represents a basis for differentiating selectedhomes based upon one of the selected attributes. For example, wherenumber of bedrooms is a selected attribute, a non-leaf node mayrepresent the test “number of bedrooms≦4.” This node defines 2 subtreesin the tree: one representing the selected homes having 4 or fewerbedrooms, the other representing the selected homes having 5 or morebedrooms. Each leaf node of the tree represents all of the selectedhomes having attributes matching the ranges of attribute valuescorresponding to the path from the tree's root node to the leaf node.The facility assigns each leaf node a value corresponding to the mean ofthe selling prices of the selected homes represented by the leaf node.

In some areas of the country, home selling prices are not publicrecords, and may be difficult or impossible to obtain. Accordingly, insome embodiments, the facility estimates the selling price of a home insuch an area based upon loan values associated with its sale and anestimated loan-to-value ratio.

In order to weight the trees of the forest, the facility further scoresthe usefulness of each tree by applying the tree to homes in the tableother than the homes that were selected to construct the tree, and, foreach such home, comparing the value indicated for the home by theclassification tree (i.e., the value of the root leaf node into whichthe tree classifies the home) to its selling price. The closer thevalues indicated by the tree to the selling prices, the higher the scorefor the tree.

In most cases, it is possible to determine the attributes of a home tobe valued. For example, they can often be obtained from existing tax orsales records maintained by local governments. Alternatively, a home'sattributes may be inputted by a person familiar with them, such as theowner, a listing agent, or a person that derives the information fromthe owner or listing agent. In order to determine a value for a homewhose attributes are known, the facility applies all of the trees of theforest to the home, so that each tree indicates a value for the home.The facility then calculates an average of these values, each weightedby the score for its tree, to obtain a value for the home. In variousembodiments, the facility presents this value to the owner of the home,a prospective buyer of the home, a real estate agent, or another personinterested in the value of the home or the value of a group of homesincluding the home.

In some embodiments, the facility applies its model to the attributes ofa large percentage of homes in a geographic area to obtain and convey anaverage home value for the homes in that area. In some embodiments, thefacility periodically determines an average home value for the homes ina geographic area, and uses them as a basis for determining andconveying a home value index for the geographic area.

Because the approach employed by the facility to determine the value ofa home does not rely on the home having recently been sold, it can beused to accurately value virtually any home whose attributes are knownor can be determined. Further, because this approach does not requirethe services of a professional appraiser, it can typically determine ahome's value quickly and inexpensively, in a manner generally free fromsubjective bias.

FIG. 1 is a block diagram showing some of the components typicallyincorporated in at least some of the computer systems and other deviceson which the facility executes. These computer systems and devices 100may include one or more central processing units (“CPUs”) 101 forexecuting computer programs; a computer memory 102 for storing programsand data—including data structures, database tables, other data tables,etc.—while they are being used; a persistent storage device 103, such asa hard drive, for persistently storing programs and data; acomputer-readable media drive 104, such as a CD-ROM drive, for readingprograms and data stored on a computer-readable medium; and a networkconnection 105 for connecting the computer system to other computersystems, such as via the Internet, to exchange programs and/ordata—including data structures. In various embodiments, the facility canbe accessed by any suitable user interface including Web services callsto suitable APIs. While computer systems configured as described aboveare typically used to support the operation of the facility, one ofordinary skill in the art will appreciate that the facility may beimplemented using devices of various types and configurations, andhaving various components.

FIG. 2 is a flow diagram showing steps typically performed by thefacility to automatically determine current values for homes in ageographic area. The facility may perform these steps for one or moregeographic areas of one or more different granularities, includingneighborhood, city, county, state, country, etc. These steps may beperformed periodically for each geographic area, such as daily. In step201, the facility selects recent sales occurring in the geographic area.The facility may use sales data obtained from a variety of public orprivate sources.

FIG. 3 is a table diagram showing sample contents of a recent salestable. The recent sales table 300 is made up of rows 301-315, eachrepresenting a home sale that occurred in a recent period of time, suchas the preceding 60 days. Each row is divided into the followingcolumns: an identifier column 321 containing an identifier for the sale;an address column 322 containing the address of the sold home; a squarefoot column 323 containing the floor area of the home; a bedrooms column324 containing the number of bedrooms in the home; a bathrooms column325 containing the number of bathrooms in the home; a floors column 326containing the number of floors in the home; a view column 327indicating whether the home has a view; a year column 328 showing theyear in which the house was constructed; a selling price column 329containing the selling price at which the home was sold; and a datecolumn 330 showing the date on which the home was sold. For example, row301 indicates that sale number 1 of the home at 111 Main St., Hendricks,Ill. 62012 having a floor area of 1850 square feet, 4 bedrooms, 2bathrooms, 2 floors, no view, built in 1953, was for $132,500, andoccurred on Jan. 3, 2005. While the contents of recent sales table 300were included to pose a comprehensible example, those skilled in the artwill appreciate that the facility can use a recent sales table havingcolumns corresponding to different and/or a larger number of attributes,as well as a larger number of rows. Attributes that may be used include,for example, construction materials, cooling technology, structure type,fireplace type, parking structure, driveway, heating technology,swimming pool type, roofing material, occupancy type, home design type,view type, view quality, lot size and dimensions, number of rooms,number of stories, school district, longitude and latitude, neighborhoodor subdivision, tax assessment, attic and other storage, etc. For avariety of reasons, certain values may be omitted from the recent salestable. In some embodiments, the facility imputes missing values usingthe median value in the same column for continuous variables, or themode (i.e., most frequent) value for categorical values.

While FIG. 3 and each of the table diagrams discussed below show a tablewhose contents and organization are designed to make them morecomprehensible by a human reader, those skilled in the art willappreciate that actual data structures used by the facility to storethis information may differ from the table shown, in that they, forexample, may be organized in a different manner; may contain more orless information than shown; may be compressed and/or encrypted; etc.

Returning to FIG. 2, in steps 202-205, the facility constructs andscores a number of trees, such as 100. This number is configurable, withlarger numbers typically yielding better results but requiring theapplication of greater computing resources. In step 203, the facilityconstructs a tree. In some embodiments, the facility constructs andapplies random forest valuation models using an R mathematical softwarepackage available at http://cran.r-project.org/ and described athttp://www.maths.lth.se/help/R/.R/library/randomForest/html/randomForest.html.Step 203 is discussed in greater detail below in connection with FIG. 4.In step 204, the facility scores the tree constructed in step 203. Step204 is discussed in greater detail below in connection with FIG. 8.

In steps 206-207, the facility uses the forest of trees constructed andscored in steps 202-205 to process requests for home valuations. Suchrequests may be individually issued by users, or issued by a program,such as a program that automatically requests valuations for all homesin the geographic area at a standard frequency, such as daily, or aprogram that requests valuations for all of the homes occurring on aparticular map in response to a request from a user to retrieve the map.In step 206, the facility receives a request for valuation identifyingthe home to be valued. In step 207, the facility applies the treesconstructed in step 203, weighted by the scores generated for them instep 204, to the attributes in the home identified in the receivedrequest in order to obtain a valuation for the home identified in therequest. After step 207, the facility continues in step 206 to receivethe next request.

Those skilled in the art will appreciate that the steps shown in FIG. 2and in each of the flow diagrams discussed below may be altered in avariety of ways. For example, the order of the steps may be rearranged;substeps may be performed in parallel; shown steps may be omitted, orother steps may be included; etc.

FIG. 4A is a flow diagram showing steps typically performed by thefacility in order to construct a tree. In step 401, the facilityrandomly selects a fraction of the recent sales in the geographic areato which the tree corresponds, as well as a fraction of the availableattributes, as a basis for the tree.

FIG. 5 is a table diagram showing sample contents of a basis tablecontaining the basis information selected for the tree. Basis table 500contains rows randomly selected from the recent sales table 300, hererows 302, 308, 209, 311, 313, and 315. The basis table further includesthe identifier column 321, address column 322, and selling price column329 from the recent sales table, as well as randomly selected columnsfor two available attributes: a bedrooms column 324 and a view column327. In various embodiments, the facility selects various fractions ofthe rows and attribute columns of the recent sales table for inclusionin the basis table; here, the fraction one third is used for both.

In some embodiments, the facility filters rows from the basis tablehaving selling prices that reflect particularly rapid appreciation ordepreciation of the home relative to its immediately-preceding sellingprice. For example, in some embodiments, the facility filters from thebasis table recent sales whose selling prices represent more than 50%annual appreciation or more than 50% annual depreciation. In otherembodiments, however, the facility initially performs the filteringdescribed above, then uses the filtered basis table to construct apreliminary model, applies the preliminary model to the unfiltered basistable, and excludes from the basis table used to construct the primarymodel those sales where the valuation produced by the preliminary modelis either more than 2 times the actual selling price or less thanone-half of the actual selling price.

Returning to FIG. 4A, in step 402, the facility creates a root node forthe tree that represents all of the basis sales contained in the basistable and the full range of each of the basis attributes.

FIG. 6 is a tree diagram showing a root node corresponding to the basistable 500. The root node 601 represents the sales having identifiers 2,8, 9, 11, 13, and 15; values of the bedrooms attribute between 1−∞; andvalues of the view attribute of yes and no.

Returning to FIG. 4A, in steps 403-407, the facility loops through eachnode of the tree, including both the root node created in step 402 andany additional nodes added to the tree in step 405. In step 404, if itis possible to “split” the node, i.e., create two children of the nodeeach representing a different subrange of an attribute value rangerepresented by the node, then the facility continues in step 405, elsethe facility continues in step 406. FIG. 4B is a flow diagram showingsteps typically performed by the facility in order to determine whetherand how to split a node of a tree. These steps generally identify apotential split opportunity having the highest information gain, anddetermine whether the information gain of that potential splitopportunity exceeds the information gain of the current node. In step451, the facility determines whether the node's population—that is, thenumber of basis sales represented by the node—satisfies a splitthreshold, such as a split threshold that requires more than three basissales. If the threshold is not satisfied, then the facility returns tostep 404 in step 452 without identifying any split opportunity, suchthat the facility will not split the node; otherwise, the facilitycontinues in step 453. Though not shown, the facility may apply avariety of other tests to determine whether the node should be split,including whether any of the selected attribute ranges represented bythe node is divisible. For example, where the selected attributes arebedrooms and view, and a node represents the ranges bedrooms=5 andview=no, none of the node's selected attribute ranges can be split.

In steps 453-455, the facility analyzes the characteristics of the nodein order to be able to compare them to characteristics of pairs ofpossible child nodes that would result from different opportunities forsplitting the node. In step 453, the facility determines the meanselling price among the sales represented by the node to obtain a nodemean selling price for the node. Applying step 453 to root node 600shown in FIG. 6, the facility determines a mean selling price for thenode as shown below in Table 1 by determining the mean of all theselling prices shown in basis table 500.

TABLE 1 1 Node mean selling price = $201,400

In step 454, the facility sums the squares of the differences betweenthe node mean selling price determined in step 454 and the selling priceof each sale represented by the node to obtain a node overall squarederror. This calculation is shown below in table 2 for root node 601.

TABLE 2 2 Sale 2 overall squared error = 160000 ($201,000 − line 1)² = 3Sale 8 overall squared error = 16002250000 ($74,900 − line 1)² = 4 Sale9 overall squared error = 2714410000 ($253,500 − line 1)² = 5 Sale 11overall squared error = 817960000 ($230,000 − line 1)² = 6 Sale 13overall squared error = 92160000 ($211,000 − line 1)² = 7 Sale 15overall squared error = 1339560000 ($238,000 − line 1)² = 8 Node overallsquared error = 20966500000In step 455, the facility divides the overall squared error by one fewerthan the number of sales represented by the node in order to obtain anode variance. The calculation of step 455 for root node 600 is shownbelow in table 3.

TABLE 3 9 Node variance = line 8/5 = 4193300000

In steps 456-460, the facility analyzes the characteristics of eachpossible split opportunity that exists in the node; that is, for eachattribute range represented by the node, any point at which that rangecould be divided. For root node 600, three such split opportunitiesexist: (1) view=no/view=yes; (2) bedrooms≦4/bedrooms>4; and (3)bedrooms≦5/bedrooms>5. In step 457, for each side of the possible splitopportunity, the facility determines the mean selling price among saleson that side to obtain a split side mean selling price. Table 4 belowshows the performance of this calculation for both sides of each of thethree possible split opportunities of root node 600.

TABLE 4 10 Split side mean selling price of view = no side of $179,225possible split opportunity 1 = mean of selling prices for sales 2, 8,11, and 13 = 11 Split side mean selling price of view = yes side of$245,750 possible split opportunity 1 = mean of selling prices for sales9 and 15 = 12 Split side mean selling price for bedrooms ≦4 side of$152,450 possible split opportunity 2 = mean of selling prices of sales8 and 11 = 13 Split side mean selling price for bedrooms >4 side of$225,875 possible split opportunity 2 = mean of selling prices of sales2, 9, 13, and 15 = 14 Split side mean selling price for bedrooms ≦5 sideof $188,475 possible split opportunity 3 = mean of selling prices ofsales 8, 11, 13, and 15 = 15 Split side mean selling price forbedrooms >5 side of $227,250 possible split opportunity 3 = mean ofselling prices of sales 2 and 9 =

In step 458, the facility sums the squares of the differences betweenthe selling price of each sale represented by the node and the splitside mean selling price on the same side of the possible splitopportunity to obtain a possible split opportunity squared error. Theresult of the calculation of step 458 for root node 600 is shown belowin table 5.

TABLE 5 16 Possible split opportunity 1 squared error for 474150625 sale2 = ($201,000 − line 10)² = 17 Possible split opportunity 1 squarederror for 10883705625 sale 8 = ($74,900 − line 10)² = 18 Possible splitopportunity 1 squared error for 60062500 sale 9 = ($253,500 − line 11)²= 19 Possible split opportunity 1 squared error for 2578100625 sale 11 =($230,000 − line 10)² = 20 Possible split opportunity 1 squared errorfor 1009650625 sale 13 = ($211,000 − line 10)² = 21 Possible splitopportunity 1 squared error for 60062500 sale 15 = ($238,000 − line 11)²= 22 Possible split opportunity 1 squared error = 15065732500 sum oflines 16-21 = 23 Possible split opportunity 2 squared error for618765625 sale 2 = ($201,000 − line 13)² = 24 Possible split opportunity2 squared error for 6014002500 sale 8 = ($74,900 − line 12)² = 25Possible split opportunity 2 squared error for 763140625 sale 9 =($253,500 − line 13)² = 26 Possible split opportunity 2 squared errorfor 6014002500 sale 11 = ($230,000 − line 12)² = 27 Possible splitopportunity 2 squared error for 221265625 sale 13 = ($211,000 − line13)² = 28 Possible split opportunity 2 squared error for 147015625 sale15 = ($238,000 − line 13)² = 29 Possible split opportunity 2 squarederror = 13778192500 sum of lines 23-28 = 30 Possible split opportunity 3squared error for 689062500 sale 2 = ($201,000 − line 15)² = 31 Possiblesplit opportunity 3 squared error for 12899280625 sale 8 = ($74,900 −line 14)² = 32 Possible split opportunity 3 squared error for 689062500sale 9 = ($253,500 − line 15)² = 33 Possible split opportunity 3 squarederror for 1724325625 sale 11 = ($230,000 − line 14)² = 34 Possible splitopportunity 3 squared error for 507375625 sale 13 = ($211,000 − line14)² = 35 Possible split opportunity 3 squared error for 2452725625 sale15 = ($238,000 − line 14)² = 36 Possible split opportunity 3 squarederror = 18961832500 sum of lines 30-35 =

In line 459, the facility divides the possible split opportunity squarederror by two less than the number of sales represented by the node toobtain a variance for the possible split opportunity. The calculation ofstep 459 is shown below for the three possible split opportunities ofroot node 600.

TABLE 6 37 Variance for possible split opportunity 1 = line 376643312522/4 = 38 Variance for possible split opportunity 2 = line 344454812529/4 = 39 Variance for possible split opportunity 3 = line 474045812536/4 =

In step 460, if another possible split opportunity remains to beprocessed, then the facility continues in step 456 to process the nextpossible split opportunity, else the facility continues in step 461.

In step 461, the facility selects the possible split opportunity havingthe lowest variance. In the example, the facility compares lines 37, 38and 39 to identify the possible split opportunity 2 as having the lowestvariance. In step 462, if the selected possible split opportunityvariance determined in step 461 is less than the node variancedetermined in step 455, then the facility continues in step 464 toreturn, identifying the split opportunity selected in step 461, else thefacility continues in step 463 to return without identifying a splitopportunity. In the example, the facility compares line 38 to line 9,and accordingly determines to split the root node in accordance withsplit opportunity 2.

Returning to FIG. 4A, in step 405, where the steps shown in FIG. 4Bdetermine that the node should be split, the facility creates a pair ofchildren for the node. Each child represents one of the subranges of thesplit opportunity identified in step 404 and the node's full range ofunselected attributes. Each child represents all basis sales whoseattributes satisfy the attribute ranges represented by the child. Step405 is discussed in greater detail below in connection with FIG. 7.

In step 406, because the node will be a leaf node, the facilitydetermines the mean selling price of basis sales represented by thenode.

In step 407, the facility processes the next node of the tree. Afterstep 407, these steps conclude.

FIG. 7 is a tree diagram showing a completed version of the sample tree.It can be seen that the facility added child nodes 702 and 703 to rootnode 601, corresponding to the subranges defined by the splitopportunity selected in step 461. Node 702 represents sales whosebedrooms attribute is less than or equal to 4, that is, between 1 and 4,as well as the full range of view attribute values represented by node601. Accordingly, node 702 represents sales 8 and 11. Because thisnumber of sales is below the threshold of 4, node 702 qualifies as aleaf node, and its valuation of $152,450 is calculated by determiningthe mean selling price of sales 8 and 11.

Node 703 represents sales with bedrooms attribute values greater than 4,that is, 5−∞. Node 703 further represents the full range of viewattributes values for node 601. Accordingly, node 703 represents sales2, 9, 13, and 15. Because this number of sales is not smaller than thethreshold number and the node's ranges are not indivisible, the facilityproceeded to consider possible split opportunities. In order to do so,the facility performs the calculation shown below in Table 7. For thefollowing two possible split opportunities: (4) view=no/view=yes; and(5) bedrooms=5/bedrooms>5.

TABLE 7 40 node mean selling price = mean of selling $225,875 prices forsales 2, 9, 13, and 15 = 41 sale 2 overall squared error = 618765625($201,000 − line 40)² = 42 sale 9 overall squared error = 76314625($253,500 − line 40)² = 43 sale 13 overall squared error = 221265625($211,000 − line 40)²= 44 sale 15 overall squared error = 147015625($238,000 − line 40)²= 45 node overall squared error = 1750187500 46node variance = line 45/3 = 583395833 47 split side mean selling priceof view = no side $206,000 of possible split opportunity 4 = meanselling prices of sales 2 and 13 = 48 split side mean selling price ofview = yes side $245,750 of possible split opportunity 4 = mean sellingprices of sales 9 and 15 = 49 split side mean selling price for bedrooms≦5 $224,500 side of possible split opportunity 5 = mean selling pricesof sales 13 and 15 = 50 split side mean selling price of bedrooms >5side $227,250 of possible split opportunity 5 = mean selling prices ofsales 2 and 9 = 51 possible split opportunity 4 squared error for25000000 sale 2 = ($201,000 − line 47)² = 52 possible split opportunity4 squared error for 60062500 sale 9 = ($253,500 − line 48)² = 53possible split opportunity 4 squared error for 25000000 sale 13 =($211,000 − line 47)² = 54 possible split opportunity 4 squared errorfor 60062500 sale 15 = ($238,000 − line 48)² = 55 possible splitopportunity 4 squared error = 17012500 sum of lines 51-54 = 56 possiblesplit opportunity 5 squared error for 689062500 sale 2 = ($201,000 −line 50)² = 57 possible split opportunity 5 squared error for 689062500sale 9 = ($253,500 − line 50)² = 58 possible split opportunity 5 squarederror for 182250000 sale 13 = ($211,000 − line 49)² = 59 possible splitopportunity 5 squared error for 182250000 sale 15 = ($238,000 − line49)² = 60 possible split opportunity 5 squared error = 1742625000 sum oflines 56-59 = 61 variance for possible split opportunity 4 = 85062500line 55/2 = 62 variance for possible split opportunity 5 = 871312500line 60/2 =

From Table 7, it can be seen that, between split opportunities 4 and 5,split opportunity 4 has the smaller variance, shown on line 61. It canfurther be seen that the variance of possible split opportunity 4 shownon line 61 is smaller than the node variance shown on line 46.Accordingly, the facility uses possible split opportunity 4 to splitnode 703, creating child nodes 704 and 705. Child node 704 representsbasis sales 2 and 13, and that attribute ranges bedrooms=5−∞ andview=no. Node 704 has a valuation of $206,000, obtained by averaging theselling prices of the base of sales 2 and 13. Node 705 represents baseof sales 9 and 15, and attribute value ranges bedrooms=5−∞ and view=yes.Node 705 has valuation $245,750, obtained by averaging the selling priceof sales 9 and 15.

In order to apply the completed tree 700 shown in FIG. 7 to obtain itsvaluation for a particular home, the facility retrieves that home'sattributes. As an example, consider a home having attribute valuesbedrooms=5 and view=yes. The facility begins at root node 601, and amongedges 711 and 712, traverses the one whose condition is satisfied by theattributes of the home. In the example, because the value of thebedroom's attribute for the home is 5, the facility traverses edge 712to node 703. In order to proceed from node 703, the facility determines,among edges 713 and 714, which edge's condition is satisfied. Becausethe home's value of the view attribute is yes, the facility traversesedge 714 to leaf node 705, and obtains a valuation for the sample homeof $245,750.

Those skilled in the art will appreciate that the tree shown in FIG. 7may not be representative in all respects of trees constructed by thefacility. For example, such trees may have a larger number of nodes,and/or a larger depth. Also, though not shown in this tree, a singleattribute may be split multiple times, i.e., in multiple levels of thetree.

FIG. 8 shows steps typically performed by the facility in order to scorea tree. In step 801, the facility identifies recent sales in thegeographic area that were not used as a basis for constructing the treein order to score the tree. In steps 802-805, the facility loops througheach sale identified in step 801. In step 803, the facility applies thetree to the attributes of the sale to obtain a value. In step 804, thefacility compares the value obtained in step 803 to the selling pricefor the sale to determine an error magnitude, dividing the differencebetween valuation and selling price by selling price. In step 806, thefacility calculates a score that is inversely related to the medianerror magnitude determined in step 804. After step 806, these stepsconclude.

FIG. 9 is a table diagram showing sample results for scoring a tree.Scoring table 900 scores tree 700 based upon the contents of recentsales table 300. The scoring table is made up of the rows of recentsales table 300 other than those used as basis sales for constructingthe tree, i.e., rows 301, 303, 304, 305, 306, 307, 310, 312, and 314. Itfurther contains the following columns from recent sales table 300:identifier column 321, address column 322, bedroom column 324, viewcolumn 327, and selling price column 329. The scoring table furthercontains a valuation column 911 containing the valuation of each homedetermined in step 803. For example, row 307 shows that the facilitydetermines the valuation of $245,750 for sale 7 using tree 700. Inparticular, the facility begins at root node 601; traverses to node 703because the number of bedrooms 5 is greater than 4; traverses to node705 because view=yes; and adopts the valuation of node 705, $245,750.Scoring table 900 further contains an error column 912 indicating thedifference between each home's valuation and selling price. For example,row 307 contains an error of 0.0685, the difference between valuation$245,750 and selling price $230,000, divided by selling price $230,000.Associated with the table is a median error field 951 containing themedian of error values in the scoring table, or 0.3734. Each tree'smedian error value is used to determine weightings for the trees thatare inversely related to their median error values. In some embodiments,the facility determines the particular tree's weighting by generating anaccuracy metric for each tree by subtracting its median error value from1, and dividing the tree's accuracy measure by the sum of all of thetrees' accuracy measures. Also, a variety of different approaches todetermine a score that is negatively correlated with the average errormay be used by the facility.

When a home is valued using the forest, the sample tree will be appliedto the attributes of the home in the same way it was applied to homes inthe scoring process described above. (If any attributes of the home aremissing, the facility typically imputes a value for the missingattribute based upon the median or mode for that attribute in the recentsales table.) The valuation produced will be averaged with thevaluations produced by the other trees of the forest. In the average,each valuation will be weighted by the score attributed by the facilityto the tree. This resultant average is presented as the valuation forthe home.

FIGS. 10-11 show ways in which valuations generated by the facility maybe presented. FIG. 10 is a display diagram showing detailed informationabout an individual home. The display 1000 includes detailed information1001 about the home. Despite the fact that the home has not been soldrecently, the facility also displays a valuation 1002 for the home,enabling prospective buyers and listing agents to gauge their interestin the home, or permitting the home's owner to gauge his interest inlisting the home for sale.

FIG. 11 is a display diagram showing a map identifying a number of homesin the same geographic area. The display 1100 shows homes 1101-1112. Thefacility also displays its valuations 1151-1162 of these homes inconnection with their location on the map. Presenting the facility'svaluations in this way permits home shoppers to obtain an overview ofthe geographic area, identify special trends within the geographic area,identify the anomalous values as good values or poor picks, etc.

In some embodiments, the valuations displayed or otherwise reported bythe facility are not the “raw” valuations directly produced by thevaluation model, but rather “smoothed” valuations that are generated byblending the raw valuation generated by the current iteration of themodel with earlier valuations. As one example, in some embodiments, thefacility generates a current smoothed valuation for a home bycalculating a weighted average of a current raw valuation and a smoothedvaluation of the same home from the immediately-preceding time period,where the prior smooth valuation is weighted more heavily than thecurrent raw valuation. In some embodiments, where new iterations of themodel are constructed and applied daily, the prior smoothed valuation isweighted 49 times as heavily as the current raw valuation; where a newiteration of the model is constructed and applied weekly, the priorsmoothed valuation is weighted 9 times as heavily as the current rawvaluation; where new iterations of the model are constructed and appliedmonthly, the previous smoothed valuation is weighted twice as heavily asthe current raw valuation. Those skilled in the art will appreciate thata variety of other smoothing techniques may be used in order to dampenerotic movement in a particular home's reported valuation over time.

In some embodiments, the facility constructs and applies compoundvaluation models to one or more geographic areas. A compound valuationmodel includes two or more separate classification tree forests, some orall of which may be applied to the attributes of a particular home inorder to value it. As one example, in some embodiments, the facilityconstructs a compound model including both a forest constructed asdescribed above (referred to as a “core forest”), as well as a separate,“high-end” forest constructed from basis sales having a selling priceabove the 97.5 percentile selling price in the geographic area. In theseembodiments, the compound model is applied as follows. First, the coreforest is applied to the attributes of a home. If the valuation producedby the core forest is no larger than the 97.5 percentile selling pricein the geographic area, then this valuation is used directly as themodel's valuation. Otherwise, the facility also applies the high-endforest to the attributes of the home. If the valuation produced by thecore forest is above the 99 percentile selling price, then the valuationproduced by the high-end forest is used directly as the model'svaluation. Otherwise, a weighted average of the valuations produced bythe core forest and the high-end forest is used, where the weight of thecore forest valuation is based upon nearness of the core model valuationto the 97.5 percentile selling price, while the weight of the high-endforest valuation is based on the nearness of the core forest valuationto the 99 percentile selling price.

Tailoring Valuation to User Input

The facility typically initiates the tailoring of a valuation for asubject home to input from the subject home's user in response toexpression of interest by the user in performing such tailoring. Invarious embodiments, the facility enables the user to express suchinterest in a variety of ways. As one example, the user may select link1011 from the display of detailed information about a particular homeshown in FIG. 10. FIGS. 12 and 13 show additional ways that the facilitypermits the user to express such interest in some embodiments. FIG. 12is a display diagram showing a display typically presented by thefacility containing the attributes of a particular home, also called“home facts.” The display 1200 includes a list 1201 of attributes andtheir values, as well as a link 1202 to display a more extensive list.The display further includes a way 1210 that the user may traverse inorder to express interest in tailoring the valuation of the home.

FIG. 13 is a display diagram showing a display typically presented bythe facility to identify possible comparable sales on a map. The display1300 includes such a map 1301 and well as a link 1310 that the user canfollow in order to express interest in tailoring evaluation of thishome.

FIG. 14 is a flow diagram showing steps typically performed by thefacility in order to tailor a valuation of a subject home based oninformation provided by the home's user. The interactions describedherein are typically performed by serving web pages to a user who is theuser of the subject home, and receiving input from that user based uponthe user's interaction with the web pages. These web pages may be partof a web site relating to aspects of residential or other real estate.FIGS. 15-19, discussed in greater detail below, contain sample displayspresented by the facility in some embodiments in performing the steps ofFIG. 14.

In step 1401, the facility displays an initial valuation of the subjecthome. In step 1402, the facility solicits updated home attributes fromthe user.

FIG. 15 is a display diagram showing a sample display typicallypresented by the facility to display an initial valuation of the subjecthome and solicit updated home attributes from the user. The display 1500includes a navigation area 1510 which includes a progress indicator madeup of step indicators 1511-1515. The display of step indicator 1511 forthe first step more prominently than the other step indicators indicatesthat the first step is presently being performed. The display furtherincludes an initial valuation 1520 in the amount of $550,727. In thisand the display diagrams that follow, home valuations are identified as“Zestimates.” The display also includes a number of controls 1531-1541,each corresponding to a different attribute or “home fact” of thesubject home. In some embodiments, attribute controls are only displayedfor attributes whose value has a non-zero influence on the valuationsprovided by the valuation model for the geographic area containing thehome, or a level of influence that exceeds a threshold larger than zero.Initially, these attribute controls are populated with attribute valuesautomatically retrieved from a data source and used to determine thesubject home's initial valuation in the manner described above. The usercan interact with any of these controls to change the correspondingattribute value. For example, the user may interact with control 1532 tocorrect the number of bedrooms from 3 to 4, or may interact with control1537 to update the indicated territorial view to a water view that wascreated when a nearby building was demolished. In some embodiments, asthe user interacts with these controls, the facility updates anindication 1550 of the extent to which the user's updates have alteredthe valuation of the home. In some embodiments, the facility determinesthis amount by determining a new valuation for the home by applying theexisting geographically-specific valuation model for the home—in otherwords, the existing forest of decision trees for the home—to the updatedattributes, and subtracting the original valuation from the result. Forexample, where the user uses control 1537 to change the value of theview attribute from territorial to none, the facility retraverses all ofthe trees of the forest constituting the model for the geographic regioncontaining the home. In particular, when the facility traverses sampletree 700 shown in FIG. 7, rather than traversing from node 703 to node705 for the home as the facility initially did based upon an affirmativevalue of the view attribute, the facility traverses from node 703 tonode 704 based upon the new negative value of the view attribute.Accordingly, the weighted average of the valuations for all the trees ofthe forest include a valuation of $206,000 from tree 700, obtained fromleaf node 704, rather than valuation of $245,750 obtained from node 705.

If the user makes a mistake, he or she can select a control 1560 inorder to restore the original facts on which the initial valuation wasbased. The user can select a control 1570 in order to update anindication 1580 of the valuation of home adjusted to take into accountthe user's updates to the attributes. In some embodiments (not shown),the facility further includes in the display a warning that, because anupdated attribute value provided by the user is not represented amongthe basis sales used to construct the valuation model, updatedvaluations based upon this updated attribute value may be inaccurate.When the user has finished updating home attributes, he or she canselect a next control 1591 to move to the next step of the process,describing home improvements.

Returning to FIG. 14, in step 1403, the facility displays a refinedvaluation that takes into account the attributes updated by the user. Instep 1404, the facility solicits information from the user aboutimprovements to the subject home.

FIG. 16 is a display diagram showing a typical display presented by thefacility to permit the user to describe improvements made to the subjecthome. The display 1600 includes a highlighted step indication 1612 thatindicates that the user is performing the second step of the process.Indication 1680 reflects the addition of $1500 to the initial valuationbased upon the attribute updates performed by the user in the first stepof the process. The display includes an area 1830 that the user can useto describe improvements to the subject home. These include animprovement type control 1631, an improvement timing control 1632, andan improvement cost control 1633. When the user interacts with thesecontrols to describe an improvement, the facility typically uses theimprovement type and the geographical region containing the subject hometo access a table containing average recovery rates for differentimprovement types and regions. The facility applies the looked-uprecovery rate to the improvement cost amount to obtain an estimatedpresent value. In some embodiments, the facility further applies adepreciation schedule to the estimated present value, such as onespecifying smooth depreciation from one hundred percent to twenty-fivepercent over the period between zero and ten years after theimprovement, and a flat twenty-five percent thereafter. In someembodiments, however, the values of various improvements areincorporated directly in the valuation model—i.e., are represented inthe trees of the forest—therefore may be handled in the application ofthe valuation model to the home, rather than computed separately. Insome embodiments, the facility further monitors for the entry of homeimprovement in display 1600 that are redundant with attribute updates inFIG. 15, and prevents them from contributing redundantly to calculatingthe overall revised value for the subject home, either by preventingsuch an entry, or by reducing the value of such an entry to avoiddouble-counting. The facility then displays an indication 1634 of anestimated present value of the improvement. The user may select an editlink 1635 to override this estimate of present value. The displayfurther includes a link 1639 that the user may follow to extend theimprovement description area for describing another improvement. Thedisplay further includes an indication 1640 of the total present valueof the described improvements. The display further includes adescription 1650 of different improvement types made available by thefacility. The user can click the next control 1691 to proceed to thenext step of the process, describing other aspects of the home thataffect its value.

Returning to FIG. 14, in step 1405, the facility displays a refinedvaluation that takes into account the improvements described by theuser. In step 1406, the facility solicits information from the userabout other factors affecting the value of the subject home.

FIG. 17 is a display diagram showing a sample display typicallypresented by the facility to enable the user to describe other aspectsof the subject home that affect its value. It can be seen thatindication 1780 of the refined value reflects the addition of $3300 forimprovements listed in the previous step. The display includes a featuredescription area 1730 for inputting information about additionalaspects. This area includes a description control 1731 for entering adescription of the aspect, the control 1732 for indicating whether theaspect adds to or subtracts from the value of the home, and a control1733 for indicating the magnitude of the impact of the aspect on thevalue of the home. The display further includes a link 1739 that theuser may traverse to expand the aspect description area to describeanother aspect. The display further includes an indication 1740 of thetotal amount added to or subtracted from the subject home's value by thedescribed aspects. The user may select next control 1791 to proceed tothe next step of the process, identifying comps regarded by the user assimilar to the subject home.

Returning to FIG. 14, in step 1407, the facility displays a refinedvaluation that takes into account the other factors described by theuser. In step 1408, the facility solicits from the user a list of nearbyhomes that have recently sold (“comps”) that are the most similar to thesubject home.

FIG. 18 is a display diagram showing a sample display presented by thefacility in order to enable the user to identify comps regarded by theuser as similar to the subject home. It can be seen that the indication1880 of refined value has been decreased by $300 to reflect a netreduction in the value corresponding to the sum of the inputted valuesfor the aspects described in the previous step of the process. Thedisplay includes a map 1830 on which possible comps are displayed asnumbers appearing in circles. For example, a possible comp 1831 appearsas a circle with the number one in it. When the user hovers over and/orclicks on one of these possible comps, the facility displays a pop-upballoon including information about the possible comp. Additionalinformation about the possible comps is also shown below in table 1840.The user can traverse link 1833 in the pop-up balloon or link 1834 inthe table in order to add the first possible comp to a “My Comps” list1835. The user populates the My Comps list in this manner, until itcontains what he or she regards as up to ten comps most similar to thesubject home.

After the user has populated the My Comps list, and selects either theupdated value control 1870 or the next control 1891, in step 1409, thefacility determines an updated valuation for the subject home based uponthe population of the My Comps list. In particular, in some embodiments,the facility makes a copy of the recent sales table 300 for thegeographic region that contains the subject home and was used toconstruct the forest for this geographic area. The facility alters thecopy of the recent sales table to increase a weighting in the copy ofthe recent sales table of the comps in the My Comps list, causing themto be significantly more likely to be selected from the copy of therecent sales table for inclusion in tree basis tables. In someembodiments, the facility achieves this weighting by adding copies ofthe rows for each comp in the My Comps list to the recent sales table.In some embodiments, the facility also increases to a lesser extent theweighting in a copy of the recent sales table of the sales of homes thatare near the subject home, such as having the same zip code, having thesame neighborhood name, or having a calculated distance from the subjecthome that is below a particular distance threshold. The facility thenuses this altered copy of the recent sales table to generate a newforest for the geographic region. The facility applies this forest,which is tailored to the comps included in the My Comps list, to theattributes of the home as updated in the first step of the process. Insome embodiments, the result of applying the tailored forest is adjustedby averaging it with a separate valuation determined by multiplying thefloor area of the subject home by an average selling price per squarefoot value among the sales on the My Comps list. In some embodiments,the facility determines the valuation by averaging the average sellingprice per square foot valuation with the original model valuation ratherthan the updated model valuation if the initial model valuation isbetween the adjusted model valuation and the average price per squarefoot valuation. The facility then subtracts from the resulting valuationthe change in value from step one—$1500 in the example—because thisamount is represented in the new valuation. To arrive at an overallvaluation, the facility adds to the result the additional amountsidentified in the second and third steps of the process, in the example$3300 and negative $300.

In some embodiments, the facility permits the user to populate the MyComps list with any similar nearby home, irrespective of whether it hasrecently been sold. The facility then emphasize the valuations of thesehomes, such as valuations automatically determined by the facility, indetermining a refined valuation for the subject home.

FIGS. 19A-19F show a sample display typically presented by the facilityin order to present an overall revised value for the subject home. FIG.19A shows the entire display, while FIGS. 19B-19F show portions of thedisplay at a greater level of magnification. The display includes anoverall summary section 1930 containing an overview of the calculationof the new revised value, as well as detailed sections 1940, 1950, 1960,and 1970, each displaying additional detail about the value added orsubtracted by each of the four steps of the process. FIG. 19B shows thatsection 1930 contains a breakdown beginning with the initial valuation1920, and adding value increments 1931-1934 for each of the four stepsof the process to arrive at the new revised value 1980. FIG. 19C showsthat the increment 1931 for the updated attributes is the result ofincreasing the number of bedrooms from 3-4 (1941) and changing the viewfrom none to water (1942). FIG. 19D shows that the value increment forhome improvements 1931 is the result of adding a value of $300 for a newroof (1951) and $3000 for a kitchen remodel (1952). FIG. 19E shows thatthe increment for other aspects affecting the value of the subject homeis arrived at by adding $700 for an orchard (1961) and subtracting $1000because a new fence is needed (1962). FIG. 19F shows that the user'sselection of comps has established an increment of $2650 (1935). Section1970 further includes a map 1971 showing the comps selected by the user,as well as a table 1972 showing the same in a different form.

In various embodiments, the behavior of the facility described above isadapted in various ways. As one adaptation, in some embodiments, thefacility uses a smoothed version of the valuation produced by thevaluation model, rather than a raw version. For example, a smoothedversion of this valuation may be obtained by blending the raw valuationproduced using a current iteration of the model with one or morevaluations produced using earlier iterations of the model. In someembodiments, such blending involves calculating a weighted average ofthe current raw valuation and the immediately-preceding smoothedvaluation in which the smoothed valuation is weighted more heavily. Forexample, where the valuation model is updated daily, in someembodiments, the facility weights the preceding smoothed valuation 49times more heavily than the current raw valuation.

As another adaptation, in some embodiments, where user input causes thefacility to produce an updated valuation for a home that varies from theoriginal valuation of the home by more than a threshold percentage, thefacility displays a warning message indicating that the valuation haschanged significantly, and may not be accurate.

As another adaptation, in some embodiments, the facility generates atailored valuation using a valuation model that is constrained to use aproper subset of available home attributes, such as only the attributeswhose values are available for the user to update in the first step ofthe process of generating the tailored valuation. In some embodiments,this involves using a separate decision tree forest valuation model thatis constructed using only the subset of attributes. In some embodiments,this involves using a valuation model of another type that isconstructed using only the subset of attributes, such as a linearregression model constructed by plotting each of the base of sales as apoint in N+1-space, where N is the number of continuous attributes inthe subset plus the sum of the unique values of categorical attributesin the subset minus the number of categorical attributes in the subset,N of the dimensions are devoted individually to the values of attributesamong the subset, and the final dimension is devoted to selling price;and using curve-fitting techniques to construct a function yielding homevalue whose independent variables are the values of the attributes amongthe subset; this function is used to determine valuations of the subjecthome.

FIG. 20 is a table diagram showing sample contents of recent salesinformation used to construct a linear regression valuation model thatis based on the attributes whose values are available for the user toupdate in the first step of the process of generating a tailoredvaluation. It can be seen that the table 2000 includes the followingcolumns for each sale: a sale id column 2021 containing an identifierfor the sale; a square foot column 2022 containing the improved floorarea of the home; a lot size column 2023 containing the area of thehome's lot, in square feet; a bedrooms column 2024 containing the numberof bedrooms in the home; a bathrooms column 2025 containing the numberof bathrooms in the home; a floors column 2026 containing the number ofstories in the home; a year column 2027 showing the year in which thehouse was constructed; a selling price column 2028 containing theselling price at which the home was sold; a roof type column 2029indicating the type of material from which the home's roof isconstructed; and a use code column 2030 containing an indication of theprimary use of the home.

Table 8 below lists variables derived from these sale attribute valuesthat are used as independent variables to construct a linear regressionmodel.

TABLE 8 63 SQUAREFEETPERBEDROOM = column 2022/column 2024 64BUILTDATEDIFFERENCEYEARS = current year − column 2027 65 BATHROOMCNT =column 2025 66 BEDROOMCNT = column 2024, or, if empty, total number ofrooms 67 FINISHEDSQUAREFEET = column 2022 68 LOTSIZESQUAREFEET = column2023 69 STORYCNT = column 2026 70 USECODETYPEIDSTANDARD = encodedversion of column 2030 71 ROOFTYPEID = encoded version of column 2029 72BEDSQFT = line 66 * line 67 73 BEDLOT = line 66 * line 68 74 SQFTLOT =line 67 * line 68 75 BED2 = (line 66)² 76 LOT2 = (line 68)² 77 YEAR2 =(line 64)² 78 SQFT2 = (line 67)²

For each of a group of recent sales, the facility creates a tuple madeup of the values of the variables showing lines 63-78 in Table 8 basedupon the sale's attribute values, as well as the selling price for thesale. The facility submits the generated tuples to a linear regressionengine, which fits a curve to the points represented by the tuples,resulting in a set of coefficients representing a linear valuationformula. For example, in some embodiments, the facility performs thecurve-fitting by invoking a lm( ) function described athttp://cran.r-project.org/.doc/manuals/R-intro.html#Linear-models, andavailable as part of the R statistical computing environment, availableat http://www.r-project.org/. This formula can then be used as avaluation model to determine a valuation for an arbitrary home, given atuple corresponding to the home's attribute values.

As an example, when the facility considers the recent sales data shownin FIG. 20, it constructs a valuation formula shown as the sum of thelines of Table 9 below.

TABLE 9 79 $219,000 80 −$16 * FINISHEDSQUAREFEET 81 −$171 *LOTSIZESQUAREFEET 82 $0 * SQFT2 83 $0 * LOT2 84 $0 * SQFTLOT 85 $2 *YEAR2 86 $1,933 * BUILTDATEDIFFERENCEYEAR 87 $4,940 * STORYCNT 88$26,100 * BATHROOMCNT 89 $35,110 * BED2 90 −$337 * BEDSQFT 91 $55 *BEDLOT 92 $62,980 * BEDROOMCNT 93 $15,390 if (ROOFTYPE = tile) 94$87,640 if (ROOFTYPE = shake)

In some embodiments, the facility filters out the recent sales data usedby the facility to generate a valuation formula sales whose attributeshave extreme values, such as an age greater than 300 years. In someembodiments, the facility tailors the valuation formula created by theprocess described above to a particular home using one or more of thefollowing techniques: more heavily weighting sales having a high sellingprice in valuation formulas constructed for valuing a home whose primaryvaluation is near the average selling price of these high-end homes;more heavily weighting recent sales that are geographically near thehome to be valued, such as in the same zip code; and, where the user hasselected particular recent sales as My Comps, more heavily weightingthese sales in constructing the valuation formula. In some embodiments,data missing from the recent sales data used to construct the valuationfunction is imputed in a manner similar to that described above.

In some embodiments, the facility employs a model of a type other thanthe primary, decision-tree forest model, but does not use it to directlygenerate valuations of the subject home. Rather, it is used to generatevaluations of the subject home before and after the user updatesattributes of the subject home, and the percentage change in thevaluation produced by the other model is applied to a valuation producedfor the subject home using the original attribute values by the primary,decision-tree forest model. Similarly, in these embodiments, thefacility may construct separate copies of the other model before andafter the performance of the fourth, My Comps step of the process useeach of the copies to value the subject home, determine the percentagechange between these valuations, and apply it to a valuation producedfor the subject home by the primary model before the fourth step of theprocess is performed.

CONCLUSION

It will be appreciated by those skilled in the art that theabove-described facility may be straightforwardly adapted or extended invarious ways. For example, the facility may use a variety of userinterfaces to collect various information usable in determiningvaluations from users and other people knowledgeable about homes. Whilethe foregoing description makes reference to particular embodiments, thescope of the invention is defined solely by the claims that follow andthe elements recited therein.

1. A method in a computing system for automatically determining a valuation for a subject home in response to input from an owner of the home, comprising: presenting a display that includes an indication of a first valuation determined for the subject home and indications of attributes of the subject home used in the determination, the indicated valuation being determined by applying to the indicated attributes a geographically-specific home valuation model is based upon a plurality of homes near the subject home recently sold; presenting a display that solicits input from the owner that updates one or more of the indicated attributes; receiving first input from the owner that updates one or more of the indicated attributes; applying the geographically-specific home valuation model to attributes of the subject home as updated by the first input to determine and display a second valuation for the subject home; presenting a display that solicits input from the owner that identifies the type, cost, and timing of one or more home improvements performed on the subject home; receiving second input from the owner that identifies the type, cost, and timing of one or more home improvements performed on the subject home; using the second input to determine and display (a) a present value of the identified home improvements and (b) a third valuation that takes into account the present value of the identified home improvements; presenting a display that solicits input from the owner that identifies other assets or liabilities of the subject home and the value attributed to them by the owner; receiving third input from the owner that identifies other aspects of the subject home affecting its value and the value attributed to them by the owner; determining a valuation adjustment corresponding to the identified aspects; displaying a fourth valuation that takes into account the determined valuation adjustment corresponding to the identified aspects; presenting a display that solicits input from the owner that identifies homes near the subject home that the owner regards as similar to the subject home; receiving fourth input from the owner that identifies homes near the subject home recently sold that the owner regards as similar to the subject home; using the fourth input to generate a tailored geographically-specific home valuation model that (1) is based upon a plurality of homes near the subject home recently sold that is a superset of the homes identified by the fourth input, but (2) places special emphasis on the homes identified by the fourth input; applying by a computer the tailored valuation model to the updated attributes of the subject home to obtain a fifth valuation of the subject home; and displaying the fifth valuation based on the application of the tailored valuation model.
 2. A computer readable medium for storing contents that causes a computing system to perform a method for procuring information about a distinguished property from its owner that is usable to refine an automatic valuation of the distinguished property, the method comprising: displaying at least a portion of information about the distinguished property used in the automatic valuation of the distinguished property; obtaining user input from the owner adjusting at least one aspect of information about the distinguished property used in the automatic valuation of the distinguished property; and displaying to the owner a refined valuation of the distinguished property that is based on the adjustment of the obtained user input.
 3. The computer-readable medium of claim 2, further comprising: determining whether any of the altered home attributes is an attribute not present among home sales used to construct the geographically-specific home valuation model; and if so, displaying a warning.
 4. The computer-readable medium of claim 2, further comprising: determining whether the refined valuation diverges from the automatic valuation by more than a threshold percentage; and if so, displaying a warning.
 5. The computer-readable medium of claim 2 wherein the adjustment of the obtained user input includes altering property attributes used in the automatic valuation of the distinguished property, and wherein the displayed refined valuation is based at least in part on the altered property attributes.
 6. The computer-readable medium of claim 2 wherein the adjustment of the obtained user input includes adding a description of an improvement to the distinguished property, and wherein the displayed refined valuation is based at least in part on a valuation of the described improvement.
 7. The computer-readable medium of claim 2 wherein the adjustment of the obtained user input includes adding a description of an aspect of the distinguished property not considered by the automatic valuation of the distinguished property and an estimate by the owner of its value, and wherein the displayed refined valuation is based at least in part on the estimate of the value of the described aspect.
 8. The computer-readable medium of claim 2 wherein the adjustment of the obtained user input includes identifying recent sales of nearby properties regarded by the owner as similar to the distinguished property, and wherein the displayed refined valuation is based at least in part on a repetition of the automatic valuation of the distinguished property in which the influence of the identified sales is magnified.
 9. The computer-readable medium of claim 8 wherein the adjustment of the obtained user input further includes identifying a scoring of the properties sold in the identified sales reflecting the relative level of similarity of the sold properties to the distinguished property, and wherein the displayed refined valuation is based at least in part on a repetition of the automatic valuation of the distinguished property in which the influence of the identified sales is magnified in a manner consistent with the identified scores.
 10. The computer-readable medium of claim 9 wherein the user input identifies a scoring of the properties sold in the identified sales reflecting the relative level of similarity of the sold properties to the distinguished property by specifying a ranked order for the identified sales.
 11. The computer-readable medium of claim 8, the method further comprising displaying a map showing properties in a geographic region surrounding the distinguished property, and wherein the owner identifies the recent sales of nearby properties regarded by the owner as similar to the distinguished property by selecting them on the displayed map.
 12. The computer-readable medium of claim 8, the method further comprising displaying a map showing properties in a geographic region surrounding the distinguished property, and wherein the owner identifies each recent sale of a nearby property regarded by the owner as similar to the distinguished property by selecting a control in a popup balloon associated with its location on the displayed map.
 13. The computer-readable medium of claim 8, the method further comprising displaying a table comprising rows each containing textual information about a different one of a plurality of recent sales of nearby properties, and wherein the owner identifies each recent sale of a nearby property regarded by the owner as similar to the distinguished property by interaction with the row containing information about the sale.
 14. The computer-readable medium of claim 2 wherein the adjustment of the obtained user input includes identifying nearby properties regarded by the owner as similar to the distinguished property, and wherein the displayed refined valuation is based at least in part on a repetition of the automatic valuation of the distinguished property in which the influence of values for the identified sales properties is magnified.
 15. A method in a computing system for refining an automatic valuation of a distinguished home based upon input from a user knowledgeable about the distinguished home, comprising: obtaining user input adjusting at least one aspect of information about the distinguished home used in the automatic valuation of the distinguished home; automatically determining a refined valuation of the distinguished home that is based on the adjustment of the obtained user input; and presenting the refined valuation of the distinguished home.
 16. The method of claim 15 wherein the presenting involves displaying the refined valuation of the distinguished home to a user providing the user input.
 17. The method of claim 15 wherein the presenting involves displaying the refined valuation of the distinguished home to a user other than the user providing the user input.
 18. The method of claim 15 wherein the automatic valuation of the distinguished home involves applying a geographically-specific home valuation model to attributes indicated by an external data source to be possessed by the distinguished home, and wherein the adjustment of the obtained user input includes altering the home attributes indicated by an external data source to be possessed by the distinguished home, and wherein the determined refined valuation is based at least in part on applying the geographically-specific home valuation model to the altered attributes.
 19. The method of claim 18 wherein the geographically-specific home valuation model is a forest of classification trees each constructed from information about recent sales of homes near the distinguished home.
 20. The method of claim 18 wherein the geographically-specific home valuation model is a linear regression model constructed from information about recent sales of homes near the distinguished home.
 21. The method of claim 18 wherein the geographically-specific home valuation model is a hybrid model, utilizing both a forest of classification trees and a linear regression-derived function, both constructed from information about recent sales of home near the distinguished home.
 22. The method of claim 21 wherein the refined valuation is determined by dividing by a first valuation of the distinguished home generated by the linear regression-derived function from the attributes indicated by the external data source to be possessed by the distinguished home a second valuation generated by the linear regression-derived function based upon the altered attributes to obtain a ratio, and wherein the ratio is multiplied by a valuation generated by the forest of classification trees based upon the home attributes indicated by the external data source to be possessed by the distinguished home.
 23. The method of claim 21, further comprising weighting in the construction of the linear regression-derived function information about recent sales of individual homes near the distinguished home based upon the extents to which the sold home and the distinguished home are similar to high-value homes near the distinguished home.
 24. The method of claim 21, further comprising weighting in the construction of the linear regression-derived function information about recent sales of individual homes near the distinguished home based upon the degree of nearness of each of the sold homes to the distinguished home.
 25. The method of claim 18 wherein the geographically-specific home valuation model is constrained to consider only home attributes available for alteration by the user.
 26. The method of claim 15 wherein the adjustment of the obtained user input includes adding a description of an improvement to the distinguished home, and wherein the determined refined valuation is based at least in part on a valuation of the described improvement.
 27. The method of claim 26 wherein the added improvement description identifies an improvement type and a cost for the described improvement, further comprising determining the valuation of the described improvement by applying a localized improvement cost recovery rate for the identified improvement type to the identified cost.
 28. The method of claim 26 wherein the added improvement description identifies an age of the described improvement and a cost for the described improvement, further comprising determining the valuation of the described improvement by applying a depreciation schedule to the identified age and cost.
 29. The method of claim 15 wherein the adjustment of the obtained user input includes adding a description of an aspect of the distinguished home not considered by the automatic valuation of the distinguished home and an estimate by a user providing the user input of its value, and wherein the determined refined valuation is based at least in part on the estimate of the value of the described aspect.
 30. The method of claim 15 wherein the automatic valuation of the distinguished home involves applying a geographically-specific home valuation model to attributes indicated by an external data source to be possessed by the distinguished home, and wherein the adjustment of the obtained user input includes identifying recent sales of nearby homes regarded as similar to the distinguished home, the method further comprising: constructing a new geographically-specific home valuation model that emphasizes the significance of the identified sales; and applying the constructed new geographically-specific home valuation model to attributes of the distinguished home to obtain a result, and wherein the determined refined valuation is based at least in part on the obtained result.
 31. The method of claim 30 wherein the constructed new geographically-specific home valuation model is applied to attributes indicated by the external data source to be possessed by the distinguished home.
 32. The method of claim 30 wherein the adjustment of the obtained user input further includes altering the home attributes indicated by the external data source to be possessed by the distinguished home, and wherein the constructed new geographically-specific home valuation model is applied to altered attributes.
 33. The method of claim 30 wherein adjustment of the obtained user input further includes identifying a scoring of the homes sold in the identified sales reflecting the relative level of similarity of the sold homes to the distinguished home, and wherein the constructed new geographically-specific home valuation model emphasizes the significance of the identified sales in a manner consistent with the identified scoring.
 34. The method of claim 30, further comprising: among the identified recent sales of nearby homes regarded as similar to the distinguished home, determining an average selling price per square foot; multiplying the determined average selling price per square foot by the floor area of the distinguished home to obtain an alternate valuation of the distinguished home; and before presenting the refined valuation of the distinguished home, blending into the refined valuation of the distinguished home the obtained alternate valuation.
 35. The method of claim 30 wherein the constructed new geographically-specific home valuation model also emphasizes the significance of sales of homes whose locations are determined to be near the location of the distinguished home.
 36. The method of claim 35 further comprising determining that the location of a home recently sold is near the location of the distinguished home if it has the same zip code as the distinguished home.
 37. The method of claim 35 further comprising determining that the location of a home recently sold is near the location of the distinguished home if it has the same neighborhood name as the distinguished home.
 38. The method of claim 30, further comprising: accessing a floor area attribute of the distinguished home and the nearby homes whose recent sales were identified, and a selling price for each of the identified sales; determining among the identified sales a selling price per square foot metric; multiplying the obtained selling price per square foot metric by the floor area of the distinguished home to obtain a product; and combining the product with the result to obtain the determined refined valuation.
 39. The method of claim 35 further comprising determining that the location of a home recently sold is near the distinguished home if the location of the distance between it and the distinguished home is less than a threshold distance.
 40. The method of claim 15 wherein the automatic valuation of the distinguished home involves applying a geographically-specific home valuation model to attributes indicated by an external data source to be possessed by the distinguished home, and wherein the adjustment of the obtained user input includes both (1) altering the home attributes indicated by an external data source to be possessed by the distinguished home and (2) adding a description of an improvement to the distinguished home, the method further comprising detecting that the alteration of home attributes and the improvement description are both directed to adding a new feature to the distinguished home, and wherein, in response to the detecting, the determined refined valuation is based at least in part on applying the geographically-specific home valuation model to the altered attributes, and is not based on the improvement description. 